Rodent trapping studies to understand the prevalence of zoonotic disease in West Africa: A scoping review.

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2021-09-24

Abstract

Rodents are important reservoirs of zoonotic infectious diseases; 255 species are known hosts of 85 zoonotic pathogens. These reservoir species are globally distributed with West Africa containing multiple known and several further predicted reservoir species. Typically International Union for Conservation of Nature (IUCN) species distribution maps and Global Biodiversity Information Facility (GBIF) presence maps are used to determine regions at risk for zoonotic spillover events, however, these datasets are biased by incomplete sampling. To investigate the impact of rodent species sampling heterogeneity on zoonotic spillover risk we have systematically reviewed rodent trapping studies to produce a contextually rich dataset that can be used to explore this risk at finer spatial resolution. Here we show that sampling of rodents and their pathogens in the West African region are spatially biased by country and trapping habitat. We found that rodent trapping effort was associated with regional population density and was increased in habitats modified by human activity. We produce updated rodent species ranges compared to available IUCN maps and enrich GBIF data by including locations of rodent species absence and trapping effort. Furthermore, we report the spatial bias in the investigation of four important rodent zoonoses, Arenaviridae, Borellia sp., Bartonella sp. and Toxoplasma sp.. Our results highlight that incorporating sampling bias is important when assessing the risk for zoonotic spillover events from rodents. The synthesis of contextually rich rodent trapping data contributes important information that is lacking in IUCN distribution maps and GBIF species presence data. We anticipate this dataset can support the production of more complete spatial risk assessments of zoonotic spillover events. For example, the inclusion of absence data and trapping effort can help identify regions where data sparsity may produce inappropriately predicted risk. Furthermore, this data specifies regions in need of comprehensive study for four targeted rodent zoonoses, Arenaviridae, Borellia sp., Bartonella sp. and Toxoplasma sp..

Introduction

The potential effect of zoonotic infectious diseases outbreaks on human health and societies has been dramatically highlighted through the ongoing SARS-CoV-2 pandemic and recent Ebola virus outbreaks. The number of zoonotic spillover events are projected to increase under intensifying anthropogenic pressure such as, increased human populations (Allen?), increasing urbanisation (Hassell?), and global climate change (Morse et al. 2012). In addition, two taxa are proposed to contribute to the highest burden of potential zoonoses globally, rodents (Rodentia) and bats (Chiroptera) (han?). Of 2,220 extant rodent species, 244 (10.7%) are described as being reservoirs of 85 zoonotic pathogens (han?). Specifically, West Africa has been identified as region at increased hazard for rodent-borne zoonotic spillover events under different projected scenarios (garcia-pena_land-use_2021?). Rodents are implicated in the transmission pathways of several diseases in this region, including, Lassa fever, Schistosomiasis and Leptospirosis.

Rodent species form diverse assemblages providing important ecosystem services such as pest regulation and seed dispersal (Fischer et al. 2018). However, the role a minority of species play in zoonotic infectious disease spillover can be considered an ecosystem disservice. Rodents typically demonstrate “fast” life histories (Dobson and Oli 2007), these traits are also associated with a species being a reservoir of zoonotic pathogens (Han et al. 2015; Albery and Becker 2021). Further, these traits are prevalent in species that thrive in human dominated landscapes where they displace species that are less likely to be reservoirs of zoonotic disease (Gibb et al. 2020). The widespread occurrence of reservoir species and their proximity to human activity make the description of rodent species assemblages and host-pathogen interactions vitally important.

Rodent trapping studies provide a useful method to describe rodent population structures and survey for potential zoonoses. Studies have been conducted in West Africa to both identify novel potential zoonotic pathogens within rodents (predicts?) and to investigate the prevalence and burden of known pathogens within known rodent hosts (e.g. for Lassa fever (Fichet-Calvet et al. 2009) and Schistosomiasis (Catalano et al. 2020)). These studies provide contextually rich information on when, where and under what conditions rodents were trapped that are typically missing from the global datasets (Bovendorp, MCCleery, and Galetti 2017). Despite this, these global datasets (e.g. IUCN, GBIF and GIDEON) are typically used to inform species distribution maps and zoonotic disease spillover predictions (Han, Kramer, and Drake 2016; Smith et al. 2014; Pigott et al. 2014). . These studies have been used to identify potential geographic hotspots where virus and host species diversity may be expected to be at its greatest to predict regional zoonotic disease spillover risks. However, there remains the potential for important confounding in these spatial distributions through bias generated by study design and selection of sampling sites. For example, systematically increased sampling (e.g. more intensive studies over longer time periods) or over-representation of certain habitats (e.g. periurban landscapes) could lead to an apparent association between locations and risk that is driven by these factors rather than an underlying host and virus association (Wille, Geoghegan, and Holmes 2021; Gibb et al. 2021). Conversely some regions may not be sampled adequately and therefore under-represented in these datasets due to sparse human populations or inaccessible habitats which may lead to proposing that these regions are at low risk of zoonotic disease spillover events.

Here, we identify rodent trapping studies performed across West Africa and identify the location and habitat types in which they have been conducted, the pathogens assessed and the host-pathogen associations that have been reported in order to quantify the potential bias and to identify regions requiring further focussed investigation.

Methods

We conducted a search in Ovid MEDLINE, Web of Science (Core collection and Zoological Record), JSTOR, BioOne, African Journals Online, Global Health and the pre-print servers, BioRxiv and EcoEvoRxiv for the following terms as keywords, no date limits were set:

1. Rodent OR Rodent trap*
2. West Africa (or the individual countries)
3. 1. AND 2.

We searched other resources including the UN Official Documents System, Open Grey, AGRIS FAO and Google Scholar using combinations of the above terms. Searches were run on 2021-03-01.

We included studies if they met all of the following inclusion criteria; i) reported findings from trapping studies where the target was a small mammal, ii) described the type of trap used or the length of trapping activity or the location of the trapping activity, iii) included trapping activity from at least one West African country, iv) recorded the genus or species of trapped individuals, v) were published in a peer-reviewed journal or as a pre-print on a digital platform or as a report by a credible organisation. We excluded studies if they met any of the following exclusion criteria: i) reported data that were duplicated from a previously included study, ii) no full text available, iii) not available in English. One reviewer screened titles, abstracts and full texts against the inclusion and exclusion criteria. At each stage, a random subset (10%) was reviewed by a second reviewer.

Data extraction

We extracted data from eligible studies into a Google sheets document. Extracted variables included i) study identifiers; ii) study aims; iii) trapping methodology; iv) geolocation data; v) method of speciation; vi) trapping locations and dates; vii) trapped species; viii) number of trap-nights and ix) microorganisms/pathogens of interest. The data extraction tool is archived and available in Supplementary Material 1.

Location of rodent trapping studies and habitats studied

We extracted GPS locations for the most precise location presented (i.e. trap, trap-line, study site or study region). We extracted coordinates in the format reported and converted them to decimal degrees. We recorded the habitat classification scheme a study used (e.g. IUCN Habitat Classification Scheme (Version 3.1)). For studies not using standardised recording, the explicit description from of the habitat in which the trap was placed was extracted. For studies reporting multiple habitat types (e.g. rice field, corn field and vegetable garden) for a single trap, trap-line or trapping grid, a higher order classification of habitat type was recorded (e.g. agricultural land).

Rodent presence, absence, abundance

We mapped genus and species names to the species names used in the Global Biodiversity Information Facility (GBIF) taxonomy (Facility 2021). We extracted information on the presence, absence and number of trapped individuals. For studies reporting on all trapped individuals (i.e. not those only reporting on the presence of a specific species of interest), the pseudo-absence of a species reported as present elsewhere in the study was explicitly recorded as an absence at that trap location.

Pathogen presence and absence

We extracted data on all pathogens assayed in studies investigating rodents for potential zoonoses. The number of rodents tested and the number of positive or negative samples were recorded alongside the type of assay used (e.g. Polymerase Chain Reaction (PCR), Enzyme Linked ImmunoSorbent Assay (ELISA) or viral culture). If studies reported indeterminate results this was noted. Where possible, pathogens were identified to species level. However, where an assay only allowed for attribution to a family of viruses or bacteria, the higher order grouping was used (e.g. Arenaviridae for a PCR using a non-specific arenavirus primer).

Analysis

Location and habitats of rodent trapping to investigate potential biases

We summarised the number of studies, the year in which trapping occurred and the country in which they were conducted. We used the GPS coordinates of single trap, trapping grid/line or study site and the number of trap nights to calculate trapping effort (trap night density) within level 2 administrative areas in West Africa. For studies not reporting the number of trap nights we imputed the number based on the median trap success rate from studies which reported the number of trap nights (matched to building or non-building based study sites). The median trap success rate for all rodents at a defined trap site was calculated separately for trap sites which included built environments and non-built environments. The number of rodents trapped at a trap site was then used in combination with these two values of trap success to impute the number of trap nights for trap sites with no reported trap nights. We summarised the habitat types of trap sites based on information reported in the study.

For the subset of studies investigating rodent zoonoses we compared the location of trapping sites with SEDAC Global Population Density estimates for 2005, the median year studies were commenced. We used a Generalised Additive Model (GAM) incorporating a spatial interaction term to investigate the association of number of trap nights and human population density. The model structure was specified as:

* *Trap night density* ~ *Tweedie*(log(Population density (2005)) + (Longitude * Latitude))   

We obtained land cover classifications from the European Space Agency Copernicus dataset at 300m2 spatial resolution (2005) we extracted the proportion of land cover classes within all regions of West Africa and all regions in which rodent trapping occurred for investigation of zoonotic diseases. We compared the proportion of possible land cover classes with those of regions where trapping occurred.

Finally, we mapped the presence and absence of rodent species and compared this to the presence and absence reported by both GBIF and IUCN to give a measure of the extent of each species range in which they have been sampled.

Rodent pathogen associations

We summarised the presence and absence of microorganisms, the assays used, their host species and the locations from where the samples were obtained. We investigated the association between rodent species and the detection of potential pathogens and report the proportion of positive and negative tests for each species and pathogen pair.

Results

Included studies

4,282 relevant citations were identified, with 126 rodent trapping studies included in narrative synthesis. The earliest included studies were published in 1974 with increasing numbers of studies being performed annually since 2005. The median length of rodent trapping activity was one year (IQR 0-2 years). The median time from completion of rodent trapping to publication is 3 years (IQR 2-5 years) (see Figure 1).

Figure 1: Each row represents one of the 126 included studies, green points designate the first year of data collection, blue points designate the end of data collection. For studies completed within one year the blue point completely overlies the green. Studies with a transparent grey point did not report the year in which trapping was conducted. The year of publication is shown by a red point.

Location and habitats of rodent trapping studies to investigate potential biases

Figure 2: Panel A: Map of West Africa, countries where rodent trapping has occurred are mapped to level 2 administrative areas (where available). These regions are coloured by the total number of trap nights performed at trap sites within their boundaries. Panel B: (n.b. will not include map in final manuscript) The number of trap nights conducted is associated with a regions population density. The population density for all regions in West Africa is shown in the yellow box plot, the population density and trap night density for each region is show on the purple scatterplot. The line of best fit is a GAM model not incorporating spatial interactions. The map panel on the right is the product of the GAM model incorporating spatial interactions. Panel C: For each of the 10 land cover classes from the ESA dataset we measure the proportion of 300m2 pixels within a level 2 administrative area. Zoonotic trapping studies occurred in regions over-representative for Cropland, Mosaic landscapes and Shrubland while being under-representative for Bare and Sparse vegetation land cover classes.

Rodent presence, absence, abundance

Figure 3: Each row corresponds to a single rodent species. The column on the left shows the presence and absence of a rodent species from the individual studies included in this review. The centre column shows the presence of a rodent species obtained from GBIF (September 2021) for records where longitude and latitude have been provided. The right column shows the range of rodent species as proposed by the IUCN (2021) (red shaded area), overlaid are the presence points from both this review and GBIF records.

Pathogen presence and absence

Figure 4: Presence/absence plots at each unique trapping site for the four most commonly assayed microorganisms Arenaviridae (top), Bartonella sp. (second row), Borrelia sp. (third row) and Toxoplasma gondii (fourth row). The tables to the right of each map highlight the 10 (or number applicable) most commonly positive and tested rodent species and genera assayed.

Host-pathogen associations

Figure 5 - Matrix heat plot Y - rodent species/genera, X - pathogen species/genera. Colour relates to proportion positive (Perhaps use bivariate colour to also highlight the number of test performed in that species).

Discussion

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